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Purpose <p>Machine learning, as an advanced technology, has achieved remarkable success across various fields due to its powerful data processing and pattern recognition capabilities. When applied to particle accelerators, it has the potential to optimize performance, enhance operational efficiency, and drive innovation in accelerator technology. However, the adoption of machine learning often necessitates extensive knowledge of algorithms and programming, which can be time-consuming and create barriers to accessibility.</p> Methods <p>To overcome these challenges, the development of the machine learning as a service for accelerators (MLaaS4ACC) system is proposed. This system is designed to simplify the use of machine learning tools for accelerator researchers, efficiently perform machine learning tasks, and continuously expand and optimize functionalities tailored to the unique requirements of accelerator systems.</p> Results and Conclusion <p>Currently, MLaaS4ACC can effectively perform several straightforward machine learning tasks. Compared to traditional methods, it reduces the time required for actual tasks and simplifies the model training process, while yielding results that are not significantly different. The model already meets the necessary requirements. Looking ahead, it is essential to enhance and expand the system in various aspects to address more complex demands. Improvements in both the performance and functionality of MLaaS4ACC are necessary to ensure it meets these evolving requirements.</p>

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Machine learning as a service system for particle accelerator and its application in CSNS

  • 期刊论文

  • 发表日期：

    2025年2月16日

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  • Hao Mei,
  • Yuliang Zhang,
  • Na Peng,
  • Sinong Cheng,
  • Yongcheng He,
  • Kangjia Xue,
  • Lin Wang,
  • Mingtao Li,
  • Xuan Wu,
  • Peng Zhu

摘要

Purpose

Machine learning, as an advanced technology, has achieved remarkable success across various fields due to its powerful data processing and pattern recognition capabilities. When applied to particle accelerators, it has the potential to optimize performance, enhance operational efficiency, and drive innovation in accelerator technology. However, the adoption of machine learning often necessitates extensive knowledge of algorithms and programming, which can be time-consuming and create barriers to accessibility.

Methods

To overcome these challenges, the development of the machine learning as a service for accelerators (MLaaS4ACC) system is proposed. This system is designed to simplify the use of machine learning tools for accelerator researchers, efficiently perform machine learning tasks, and continuously expand and optimize functionalities tailored to the unique requirements of accelerator systems.

Results and Conclusion

Currently, MLaaS4ACC can effectively perform several straightforward machine learning tasks. Compared to traditional methods, it reduces the time required for actual tasks and simplifies the model training process, while yielding results that are not significantly different. The model already meets the necessary requirements. Looking ahead, it is essential to enhance and expand the system in various aspects to address more complex demands. Improvements in both the performance and functionality of MLaaS4ACC are necessary to ensure it meets these evolving requirements.